Package tracking systems and methods

ABSTRACT

Methods and systems for tracking a package within an area obtain package identification information from a scannable medium associated with the package and image information of the area acquired by an optical sensing device. Image processing detects the presence and location of an object in the area that matches the package based on a comparison of the package identification information obtained from the scannable medium with the image information of the area acquired by the optical sensing device. The package is registered as being present in the area at the location of the detected object in response to detecting a match between the object and the package.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/091,180 filed Apr. 5, 2016, titled “Package Tracking Systems andMethods,” which claims the benefit of and priority to co-pending U.S.provisional application No. 62/143,332, filed Apr. 6, 2015, titled“Package Tracking System using Sensors,” and to co-pending U.S.provisional application No. 62/221,855, filed Sep. 22, 2015, titled“Package Tracking System using Sensors,” the entireties of whichapplications are incorporated by reference herein.

FIELD OF THE INVENTION

The invention relates generally to systems and methods of trackingpackages and other assets.

BACKGROUND

The shipping of packages, including, but not limited to, letters,parcels, containers, and boxes of any shape and size, is big business,one that grows annually because of online shopping. Every day, peopleand businesses from diverse locations throughout the world ship millionsof packages. Efficient and precise delivery of such packages to theircorrect destinations entails complex logistics.

Most package shippers currently use barcodes on packages to trackmovement of the packages through their delivery system. Each barcodestores information about its package; such information may include thedimensions of the package, its weight and destination. When shippingpersonnel pick up a package, he or she scans the barcode to sort thepackage appropriately. The delivery system uses this scanned informationto track movement of the package.

For example, upon arriving at the city of final destination, a packagerolls off a truck or plane on a roller belt. Personnel scan the package,and the system recognizes that the package is at the city of finaldestination. The system assigns the package to an appropriate deliverytruck with an objective of having delivery drivers operating at maximumefficiency. An employee loads the delivery truck, scanning the packagewhile loading it onto the truck. The scanning operates to identify thepackage as “out for delivery”. The driver of the delivery truck alsoscans the package upon delivery to notify the package-delivery systemthat the package has reached its final destination.

Such a package-delivery system provides discrete data points fortracking packages, but it has its weaknesses: there can be instanceswhere the position or even the existence of the package is unknown. Forexample, a package loader may scan a package for loading on deliverytruck A, but the package loader may place the package erroneously ondelivery truck B. In the previously described package-delivery system,there is no way to prevent or quickly discover this error.

Further, package-delivery systems can be inefficient. Instructions oftendirect the person who is loading a delivery truck to load it foroptimized delivery. This person is usually not the delivery person.Thus, his or her perception of an efficient loading strategy may differgreatly from that of the person unloading the vehicle. Further,different loaders may pack a vehicle differently. Additionally, theloader may toss packages into the truck or misplace them. Packages mayalso shift during transit. Time expended by drivers searching forpackages in a truck is expended cost and an inefficiency thatfinancially impacts the shippers.

Industry has made attempts to track packages efficiently. One suchattempt places RFID (Radio Frequency Identification) chips on thepackages. Such a solution requires additional systems and hardware. Forinstance, this solution requires the placement of an RFID tag on everypackage and the use of readers by package loaders or the placement ofreaders throughout the facility to track packages.

SUMMARY

All examples and features mentioned below can be combined in anytechnically possible way.

In one aspect, a method for tracking a package within an area comprisesobtaining package identification information from a barcode on apackage, acquiring image information of the area, detecting a presenceand location of an object in the area that matches the package based ona comparison of the package identification information obtained from thebarcode with the image information of the area obtained by the opticalsensing device, and registering the package as being present in the areaat the location of the detected object in response to detecting a matchbetween the object and the package.

In another aspect, a method of loading of a package into an areacomprises determining package identification information from ascannable image associated with a package, acquiring three-dimensional(3D) image information of an area, determining a location in the areafor placement of the package based on the package identificationinformation and the 3D image information, and providing an indicator ofthe location in the area determined for placement of the package.

In still another aspect, a method of loading of a package into an areacomprises determining a destination of a package to be transported basedon package identification information obtained from a scannable imageassociated with the package, designating the package for loading on agiven delivery vehicle based on the determined destination, associatingthe given delivery vehicle with a particular color, and illuminating thepackage with light of the particular color associated with the givendelivery vehicle to guide loading of the package onto that deliveryvehicle.

In one aspect, a package tracking system for use with a delivery vehiclecomprises memory storing package identification information relating toa package that is to be loaded on the delivery vehicle. At least twooptical sensing devices are positioned in the delivery vehicle to viewan area inside of the delivery vehicle. An image processor is incommunication with the at least two optical sensing devices. The imageprocessor is configured to detect a presence and location of an objectloaded in the area inside of the delivery vehicle and to determineinformation about the object. The image processor compares the storedpackage identification information to the information determined aboutthe object, to determine if the stored package identificationinformation substantially matches the information determined about theobject.

In another aspect, a package tracking system comprises at least onecamera disposed in a delivery vehicle and configured to view an areainside of the delivery vehicle. At least one depth sensor is disposed inthe delivery vehicle and configured to view the area inside of thedelivery vehicle. An image processor is in communication with the atleast one camera and the at least one depth sensor. The image processoris configured to generate three-dimensional image information of thearea based on two-dimensional images of the area received from the atleast one camera and depth images of the area received from the at leastone depth sensor. The image processor is further configured to determinea location of a package in the area based on the generatedthree-dimensional image information and package identificationinformation associated with the package.

In still yet another aspect, a package tracking system comprises atleast one camera disposed in a delivery vehicle and configured to viewan area of the delivery vehicle. At least one depth sensor is disposedin the delivery vehicle and configured to view the area. An imageprocessor is in communication with the at least one camera and the atleast one depth sensor. The image processor is configured to generatethree-dimensional image information of the area based on two-dimensionalimages of the area received from the at least one camera and depthimages of the area received from the at least one depth sensor. Theimage processor is further configured to determine a location forplacement of a package in the area based on the generatedthree-dimensional image information and package identificationinformation associated with the package.

In yet another aspect, a real-time package tracking system comprises atleast one optical sensor configured to capture images continuouslywithin a field of view of an area, and an image processor incommunication with the at least one optical sensor to receive therefromcaptured images. The image processor is configured to detect andidentify, in real time, an object entering into or leaving the field ofview based on the received captured images.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and further advantages of this invention may be betterunderstood by referring to the following description in conjunction withthe accompanying drawings, in which like numerals indicate likestructural elements and features in various figures. The drawings arenot necessarily to scale, emphasis instead being placed uponillustrating the principles of the invention.

FIG. 1 is a view of an embodiment of a package tracking system.

FIG. 2 is a diagram of an example implementation of the package trackingsystem within a delivery system.

FIG. 3 is a flow diagram of an embodiment of a process for generalpackage tracking.

FIG. 4A is a diagram illustrating an example of a match between adetected package and a scanned package.

FIG. 4B is a diagram illustrating an example of a mismatch between adetected package and a scanned package.

FIG. 5 is a flow diagram of an embodiment of an image-processing processfor identifying and matching a package.

FIGS. 6A, 6B, and 6C together are a flow diagram of an embodiment of animage-processing process that uses depth information to track a package.

FIG. 7 is a diagram of embodiments of a package tracking system thatuses radio frequency position determinations in conjunction with opticaltracking.

FIG. 8 is a schematic for an embodiment of a package tracking system.

DETAILED DESCRIPTION

Package tracking systems described herein actively tracking packagescontinuously. Advantageously such systems may not require majoralterations in personnel behavior and can be implemented with lowhardware cost. In general, these systems employ cameras, depth sensors,or other optical sensors (herein referred to generally as cameras) totrack packages, objects, assets, or items (herein referred to generallyas packages). The cameras are placed in or adjacent to the holding areafor the packages, for example, the cargo bay of a delivery vehicle or apackage room. One or more cameras can also be situated near a packageconveyor or roller belt, to track the movement of packages opticallybefore the packages are placed into a holding area. A package barcode isscanned in conjunction with it being moved into the holding area. Asused herein, a barcode is any readable or scannable medium, examples ofwhich include, but are not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor media, or any suitablecombination thereof. Package identification information about thepackage is determined from scanning the package barcode. Such packageidentification information typically includes dimensions, weight,contents or other information that may be utilized to detect and trackthe package.

An image processor analyzes the video stream from the cameras associatedwith the holding area to detect the presence of the package(s) containedwithin. When a package is identified, the image processor determines ifthe package corresponds to the package data derived from the packagebarcode. If the package barcode data and package image data match with ahigh degree of confidence, the system marks the package as existingwithin the camera area of coverage (e.g., within the delivery vehicle).Any user that thereafter views a stream of the camera view or a staticimage of the packages inside the holding area may receive an overlaythat identifies the packages contained therein and their preciselocation.

A package tracking system can also employ one or more guidancemechanisms (e.g., audible, visual) to guide placement of a package intoa holding area or to bring attention to the present location of apackage (e.g., for purposes of removal).

FIG. 1 shows a view of one embodiment of a package tracking system 100deployed in a tracking area 112. Example embodiments of the trackingarea 112 include, but are not limited to, the cargo bay of a deliverytruck or water-going vessel, a storage room, and a warehouse. Forillustrative purposes, the tracking area 112 includes a plurality ofshelves 114-1, 114-n (generally, shelf or shelves 114), and on theshelves 114 are packages and/or assets 116-1, 116-2, 116-3, . . . 116-n(generally, package 116).

Shipper systems typically identify and track packages 116 usingbarcodes. A barcode is placed on a package 116 when the shipper takespossession of the package. The barcode includes package identificationinformation about the package, including the package dimensions,identification number, delivery address, shipping route and other data.The term barcode is to be broadly understood herein to include images ormarkings on a package that contain information or data (coded orotherwise) pertaining to the package. The barcode on the package isinitially scanned into the system 100 with a scanner 124.

In general, the scanner 124 may be optical, magnetic, or electromagneticmeans, depending on the type of barcode on the package. The scanner 124may be a conventional barcode scanner or a smart phone or tablet-likedevice. The form factor of the scanner 124 is not limiting. Exampleembodiments of the scanner 124 and techniques for wirelessly trackingthe scanner 124 are described in U.S. patent application Ser. No.14/568,468, filed Dec. 12, 2014, titled “Tracking System with MobileReader,” the entirety of which is incorporated by reference herein.

The system 100 includes an optical system. In this embodiment, theoptical system includes four optical sensors represented by cameras118-1, 118-2, 118-3, and 118-4 (generally, camera 118). Each camera 118has a field of view 120 covering a portion of the area within which thepackages 116 lie (to simplify the illustration, only one field of viewis shown). An appropriate number of cameras 118 can be mounted insidethe tracking area 112 in such a way to provide a complete field of view,or at least a functionally sufficient field of view, of the area 112,and, in some cases, of an area outside the area 112 (e.g., a conveyorbelt moving the packages prior to loading). Before the system 100 beginsto operate, each camera position is fixed to ensure the camera(s) coverthe tracking area 112. The exact position and number of cameras 118 iswithin the discretion of the system designer.

The camera 118 may be a simple image or video capture camera in thevisual range, an infrared light detection sensor, depth sensor, or otheroptical sensing approach. In general, this camera enables real-timepackage tracking when the package is within the camera's area ofcoverage. The area of coverage is preferably the shelves 114 andtracking area 112. In some instances, the field of view can extendbeyond the tracking area 112, to ensure that the packages scannedoutside the tracking area 112 correspond to those packages placed insidethe tracking area 112.

In addition, each camera 118 is in communication with a processor 122(CPU 122), for example, a DSP (digital signal processor) or a generalprocessor of greater or lesser capability than a DSP. In one embodiment,the CPU 122 is a Raspberry Pi. Although shown as a single CPU within thetracking area 112, the processor 122 can be a processing systemcomprised of one or more processors inside the tracking area, outside ofthe tracking area, or a combination thereof. Communication between thecameras 118 and the CPU 122 is by way of a wired or wireless path or acombination thereof. The protocol for communicating images, thecompression of image data (if desired), and the image quality requiredare within the scope of the designer.

In one embodiment, the cameras 118 are video cameras running inparallel, and the cameras simultaneously provide images to the CPU 122,which performs an image processing solution. For this approach, theimages are merged into a pre-determined map or layout of the trackingarea 112 and used like a panorama. (Alternatively, or additionally, theCPU 122 can merge the images into a mosaic, as described in more detailbelow). The camera images are synchronized to fit the map and operate asone camera with a panorama view. In this embodiment, two (or more)cameras capture two different perspectives and the CPU 122 flattens theimages by removing perspective distortion in each of them and merges theresulting image into the pre-determined map.

An image stitching process usually first performs image alignment usingalgorithms that can discover the relationships among images with varyingdegrees of overlap. These algorithms are suited for applications such asvideo stabilization, summarization, and the creation of panoramicmosaics, which can be used in the images taken from the cameras 118(i.e., optical sensors) in the described system.

After alignment is complete, image-stitching algorithms take theestimates produced by such algorithms and blend the images in a seamlessmanner, while taking care of potential problems, such as blurring orghosting caused by parallax and scene movement as well as varying imageexposures inside the environment at which the cameras are placed in.Example image stitching processes are described in “Image Alignment andStitching: A Tutorial”, by Richard Szeliski, Dec. 10, 2006, TechnicalReport, MSR-TR-2004-92, Microsoft Research; “Automatic Panoramic ImageStitching using Invariant Features,” by Brown and D. Lowe, InternationalJournal of Computer Vision, 74(1), pages 59-73, 2007; and “PerformanceEvaluation of Color Correction Approaches for Automatic Multiview Imageand Video Stitching,” by Wei Xu and Jane Mulligan, In Intl. Conf onComputer Vision and Pattern Recognition (CVPR10), San Francisco, Calif.,2010, the teachings of which are incorporated by reference herein intheir entireties.

In an alternative embodiment, a mosaic approach may be utilized tointegrate camera images. In this embodiment, one camera 118 is used fora certain area, a second (or third or fourth) camera 118 is used foranother area, and a handoff is used during the tracking, with the imagesfrom cameras 118 being run in parallel on the CPU 122. In a mosaic, likea panorama approach, image data from the multiple cameras (or from othersensors) are merged into the map of the tracking area 112 (e.g., truck,container, plane, etc.) with each viewpoint designated for the area thatis seen by the camera 18. It will be recognized that in bothembodiments, a handoff is made when objects move from one viewpoint toanother or are seen by one camera and not the others. These handoffs maybe made using the images running in parallel on the cameras 118, withthe package placement and movement determined by the CPU 122 usingwhichever camera has the best view of the package 116.

In an alternative embodiment, if the system 100 is using depth sensors,the image stitching operation can be omitted and each camera stream datais processed independently for change, object detection and recognition.Then, the result “areas of interest” are converted to individual pointclouds (described further in connection with FIG. 6C) and transformed into a single common coordinate system. The translation and rotationtransformations used for this process are based on the camera sensorsposition and orientation in relations with each other. One camera ispicked as the main sensor and all other camera data is transformed intothe main coordinate system, achieving the same end result as the imagestitching procedure, namely, unification of package coordinates betweensensors.

In one embodiment, the image processing is performed by the CPU 122.Alternatively, if bandwidth is not a significant concern, the image datacan be transferred to a central server (FIG. 2 ) and image processingmay be performed by the central server. Those of ordinary skill in theart will recognize that any controller, CPU, graphics processor or othercomputing device capable of processing image data to perform the imageanalysis described herein may be utilized.

The image processing CPU 122 creates the aforementioned map of thetracking area 112 under surveillance. Locating the shelves 114 assiststhe image processor 112 identification edge locations of packages 116.Further, a priori calculation of the distance of each camera 18 fromshelves 114 assists in properly calculating package dimensions. In oneembodiment, a single reference dimension is needed and dimensions of atracked asset 116 can be determined at any position in space relative tothe known dimension. In case of image or video cameras only, a dimensionreference has to be related to position in the tracking area 112 (i.e.,the length and depth of the shelves are known, thus the dimensions of apackage placed on these shelves can be determined in relation with theseshelves). In this embodiment, pixel count or vector distances ofcontours of these pixels can represent the package 116 and be used tohelp determine relevant package dimension data.

FIG. 2 shows an example of an implementation of the package trackingsystem 100 (FIG. 1 ) within a delivery system 200. For illustrationpurposes, the delivery system 200 includes multiple delivery vehicles202-1, 202-n (generally, 202) and scanners 124-1, 124-n (generally, 124)used by personnel to obtain package identification information frompackages. Although shown in FIG. 2 as trucks, a delivery vehicle 202 maybe any form of transport, including, but not limited to, an airplane,automobile, van, sea-going vessel, train, airplane baggage cart. Thedelivery vehicles 202 and scanners 124 are in communication with acentral server (or servers) 204 over communication connections 206. Theserver 204 (or servers) can be cloud based, meaning that a provider ofthe server 204 makes applications, services, and resources available ondemand to users over a network (e.g., the Internet). The communicationconnections 206 may be established using any type of communicationsystem including, but not limited to, a cellular network, privatenetwork, local network, wired network, wireless network, or anycombination thereof.

The scanners 124 are in communication with the central server 204,either continuously or through data dumps, to transfer packageidentification information when a barcode on a package is scanned andthe location. Typically, the location of the scanner 124 is generic(e.g., “Atlanta”).

Each delivery vehicle 202 includes a tracking area 112, containingpackages 116, and a processor 122. Each delivery vehicle 202 may have aGPS system (FIG. 7 ) for use in directing and tracking the vehicle 202.The cloud-based server 204 (or a central controller, not shown)identifies the appropriate shipping route, and the next appropriatedelivery vehicle, if any. The delivery vehicles 202 may also communicatedata (e.g., package identification information) to the central server204. The transfer of data between the vehicles 202 and the centralserver 204, like the scanners, can be continuous or intermittent (e.g.,data dumps). Based on such communications, the central server 204 notonly can track the delivery vehicles 202, but also the progress of thepackages 116 they carry through the shipping route. The central server204 can use the package identification information to notify the driverof the next appropriate delivery vehicle, through the scanner of thedriver, to expect the package.

FIG. 3 shows an embodiment of a process 300 for general packagetracking. For purposes of illustrating the process 300 by example,reference is made to the delivery vehicle 202-1 and other elements ofFIG. 2 . Before loading a package 116-1 onto the delivery vehicle 202-1,a loader uses a scanner 124-1 to scan (step 302) a barcode associatedwith the package 116-1. The scanner 124 transmits (step 304) the barcode(package identification) information to the image processing CPU 122 ofthe delivery vehicle 202-1 or to the central server 204, which can thentransmit the data to the CPU 122. Transmission of this information maybe by Bluetooth, WIFI or other communication protocols, wired orwireless. By receiving the barcode information (e.g., identificationnumber, size, color) describing the package 116-1, the image processingCPU 122 becomes notified (step 306) of the package 116-1 and expectsthis package 116-1 to be subsequently loaded onto the delivery vehicle202-1. A loader places (step 308) the package 116-1 on a shelf of thevehicle 202-1. Light-based guidance may be used to direct the loader tothe particular vehicle 202-1 upon which to load the package, theparticular location on the shelf where to place the package 116-1, orboth.

The image processing CPU 122 detects (step 310) the presence of thepackage 116-1, as described in more detail in connection with FIG. 5 .The image processing CPU 122 then attempts to identify (step 312) thedetected package as that package expected to be loaded (i.e., from step306). Identifying the package 116-1 generally entails comparing certainvisible characteristics of the package 116-1 to certain barcodeinformation obtained during the scanning operation. In one embodiment,the size of the package measured using the camera(s) 118 of the deliveryvehicle 202-1 is compared to the expected package dimensions as readfrom the barcode. In another embodiment, the image processor 122registers the package 116-1 by virtue of the package 116-1 being thefirst package detected after notification (at step 306) of the package116-1 being scanned. In such an instance, the image processor 122 canregister the package 116-1 by associating image data captured by thecamera(s) with the identification number read from the barcode of thedetected package 116-1.

FIG. 4A shows an example of when such a comparison produces a match,thereby signifying a high level of confidence that the appropriatepackage was loaded on the delivery vehicle 202-1. In this example, thescanned barcode data identify the package 116-1 to be loaded as havingpackage dimensions of 10″ by 20″. The images captured by the camera(s)118 on the delivery vehicle 202-1 indicate that a package withdimensions of 9.8″ by 19.7″ was loaded on the delivery vehicle 202-1.The image processing CPU 122 is configured to consider the differencesbetween the dimensions of the captured images and the dimensionsaccording to the barcode data to fall within acceptable criteria fordeclaring a match.

FIG. 4B shows an example of when a comparison does not produce a match.In this example, a 10″ by 20″ package is scanned, but subsequent imagecapture data shows that a 7.4″ by 12.3″ package was loaded onto thedelivery vehicle 202-1. The image processing CPU 122 can be configuredto consider the differences between the dimensions to be too great toconsider the detected package as a match to the scanned package.

Referring back to FIG. 3 , if the data captured by the barcode scannermatches (within a predetermined threshold) the package image datacaptured by the camera 118, a match occurs. The matched package is notonly marked or identified in real time as being within the deliveryvehicle 202-1, but also the exact location of the package 116-1 in thevehicle may be made continuously available to the central server 204,loader, driver or anyone else with access to the system 200. Thisinformation, which may be referred to hereafter as package locationdata, can be stored on memory associated with the image processing CPU122. Package location data includes the dimension information detectedfor the matched package associated with the location of the packagewithin the delivery vehicle 202-1. More specifically, the imageprocessing CPU 122 may overlay the initially created vehicle map withthe package identification information in the corresponding location. Ifcommunications allow, marked package location data may be stored inmemory at other locations, including (or additionally) in the centralserver 204.

As stated previously, the image processing CPU 122 includes wirelesscommunication (commonly Bluetooth, Wi-Fi, or other communication methodsand protocols suitable for the size of the area of coverage of thecamera). The image processing CPU 122 continuously receives (step 314)real-time views captured by the cameras 118 in the delivery vehicle202-1. Because the location of the matched package is stored in memoryof the image processing CPU, the real-time image data from the camera118 is streamed to a handheld or fixed or mounted view screen to showthe live view of the package overlaid with augmented reality markingsidentifying the package. The image processing CPU 122 continuouslymonitors and tracks (step 314) within the vehicle 202-1 until motion ofan object is detected (step 316). In response to the detection ofmotion, the process 300 returns to detecting packages at step 310.

Implications of such real-time tracking can be appreciated by thefollowing illustration. A driver entering the delivery vehicle 202-1 maynot and need not have any personal knowledge of what packages wereloaded where in the vehicle. Instead, the driver carries a view screen(often in the form of a handheld tablet, smartphone, or scanner) thatdisplays a stream of one of the cameras 118 in the cargo bay of thevehicle 202-1. The image appearing on the view screen includes marksidentifying various packages. A mark may be a box around the live viewof the package with text stating the package name, identifier, intendedaddressee or most efficient package identifier. Upon arriving at a stopfor an intended package addressee, for example Mr. Jones, the driver canwalk to the back of the delivery vehicle. The system 200 mayautomatically display the package(s) intended for delivery to Mr. Jonesusing highlighting or demarcating for easy location. Alternatively, thedriver can search the image data on the view screen for markings labeled“Jones” and such packages are be demarcated on the view screen for easylocation. In addition, the system 200 may employ light-based guidance toshow the driver the location of the package.

In some embodiments, multiple live streams of the cargo in a vehicle areavailable, with one camera (e.g., 118-1 of FIG. 1 ) covering one area ofthe cargo bay and another camera (e.g., 118-2 of FIG. 2 ) coveringanother area of the cargo bay. The system 200 can thus quickly andeffectively permit a loader or delivery person who enters the cargo areato locate a package using the camera stream overlaid with packagemarking (location). For a person using a tablet viewing the cargo area,the “video stream” in one embodiment can be a static image of thedelivery vehicle sent from the image processing CPU. Since the centralmap of the delivery vehicle can be used for positioning the packages,that central map, with the location of each package of interest, is whatis used for viewing on a device.

FIG. 5 shows an embodiment of an image-processing process 500 foridentifying and matching a package. In a description of the process 500,reference is made to elements of FIG. 1 . At step 502, color data (e.g.,RGB) for at least two image frames (N and N−1) are acquired from acamera 118. The color data is converted (step 504) to grey scale datafor the at least two image frames. Those of ordinary skill in the artare familiar with producing grey scale data from color image sensors.

At step 506, an absolute difference is determined across the two imagesto detect the presence of new objects. To quicken the processing,threshold detection (step 508) may be utilized to detect regions ofinterest. In addition, in those regions of interest data may be filtered(step 510) to limit the amount of data processed. After filtering,threshold detection (step 512) may be utilized on the filtered data.

At step 514, if no changes between the grayscale images are found, thisindicates a high probability of no new package being located; the system100 does not identify or mark a package. For instance, the loader maynot have moved or loaded a package, or a new package cannot be located.The system 100 then acquires (step 502) the next temporal two frames (Nand N+1). Sampling frequency may be continuous or at regular intervalsaccording to designer preference, available processing power, andbandwidth.

If a change in the images (N and N−1) is detected at step 514, furtheranalysis occurs. For example, the change detected by the system 100 maybe the detection of the presence of the loader in the image.Alternatively, if changes in the images are indicative of a packagemoving, the image processing CPU 122 also continues to work on thecurrent image data (frame N and N−1).

Those of ordinary skill in the art will recognize that a variety ofimages may be compared to determine loading or movement of a package.For example, an N ‘current frame’ and N-X ‘previous frame’ may be testedfor motion, where Xis greater than 1, and if motion occurs then the N-Xframe (before motion occurred) may be saved as a background frame forlater processing in comparison to a more recent image frame (i.e., a newN ‘current frame’). After motion is stopped, the background frame and anew N current frame are used for package location and identification.

Whenever a new package is located, the package is to be identified. Inone embodiment, the image processing CPU 122 uses edge detection todetermine (step 516) the dimensions of the package. Objects that are notcompatible with being a package are filtered at this point. For example,if an object size is less than the smallest possible package, the objectis ignored. The system 100 can also filter other objects of a size,dimension, or location that do not correspond to a package (e.g., theloader or a clipboard or tablet carried by the loader).

Various metrics may be utilized in addition to or conjunction with thosedescribed above to aid in identifying a package. For example, any objectplaced on a shelf (mapped as described above) may be weighted logicallyso as to be presumed to be the last scanned package. The package size,color (if cameras are color), contours or other distinguishingcharacteristics may be compared to any data captured by the barcodescanner. As previously described, when a package barcode is scanned, thesystem 100 expects that the next package detected will match the scannedpackage. Reliance on this assumption is accurate provided loaders handlepackages sequentially, that is, a barcode of a package is scanned andthen that package is sorted and moved appropriately. This a prioriknowledge facilitates package identification.

At step 518, the package dimensions are used to match the package to thescanned barcode data, as described previously in connection with FIG. 3. The size of a package as determined from image data is compared to thepredicted package size based on barcode-scanned data to determine apackage match. If the match occurs, the system 100 marks (step 520) theloaded package as identified as described in more detail below. Thesystem 100 provides a cue to anyone entering the cargo area of adelivery vehicle as to the location and identification of packages keptwithin.

In addition to view screens, other package location identificationmethods can be used to improve the locating process. For example, as avehicle arrives at the destination address for the delivery of a certainpackage, a light projector (LED, laser or other) can be used to shinefocused light, or a particular color light, on the location of thepackage within the cargo area to show the delivery person exactly wherethe “matched” package is in the vehicle. The focused light can bealtered to change colors, blink, flash, or shine a pattern to signaladditional information to the delivery person, for example, prioritiesof delivery and warnings of weight, or to signify that the package ofinterest is behind or under another package. Information is directlyoverlaid on the package that to be picked up, without needing any otherscreen or sound interface that might consume time to read or hear andconsequently prolong the delivery process.

The above discussion assumes that a package that is scanned isrelatively quickly matched to a package placed in the delivery vehicle.However, there may be instances where no match occurs or where a delayin matching occurs. This may occur if the package is loaded on the wrongtruck, the driver scans one package but loads a different package, thedriver tosses a package into the truck but not within video coverage(e.g., the package is occluded from view) or the driver's body occludesvideo coverage of a package.

In such situations, an embodiment of the system 100 requires adeliverable (i.e., a particular outcome) after a package is scanned. Forexample, if no package is detected that matches the scanned package, thesystem 100 may disallow further packages from being scanned, the system100 may mark the package as scanned but unidentified, issue a warning tothe loader, notify a central server of an unidentified package, or anycombination thereof. The system designer may choose how rigidly torequire package identification and processing (i.e., no further scanninguntil the package is appropriately tracked or just marking the packageas scanned but with an unconfirmed loading status).

In some situations, a package may be loaded without having been scanned.This may be a loader error, where the loader places the package on thewrong truck, or may be intentional as in the case of theft. In thesesituations, the image processing CPU 122 still recognizes the existenceof a loaded package, but there will be no “match” of the loaded packageto a scanned package. Such a package may be “marked” in image streams as“unidentified”, instead of with data identifying the package, and thesystem may issue a “warning” to the loader (visual/auditory or other)that an unidentified package is in the vehicle. The warnings may allowthe loader (or driver) to correct the issue by scanning the package,placing the package in the camera view and producing an appropriatelymatched package. Alternatively, the system 100 may be constructed todisallow further scanning of packages if such errors occur, may issuewarnings, may send the errors to the central server, or any combinationthereof. In one example of an unidentified package being loaded into adelivery vehicle, the driver upon first entering the delivery vehiclemay receive a notice that 300 packages have been loaded in the vehicle,but that one of the packages is “unidentified”. The driver's tablet canshow the location of the unidentified package, and remedial action maybe suggested to, or required from, the driver. Alternatively, a distinctlight (i.e., red light) may be directed onto the location where theunidentified package rests.

Detection of a package may be delayed or inhibited by occlusion of thefield of view (such as the loader's body or another package). Throughprediction from threshold detection from the loader position inside thevehicle cargo area and the vehicle cargo area map already stored by CPU122, the system 100 can compare the known map of the vehicle cargo spacebefore the loader enters with a package with the new map of the vehiclecargo space after the loader places a package in the cargo area todetermine the location of the package. Thus, even if the loader's bodytemporarily occludes optical tracking as the package is placed insidethe cargo area, the package can be located, identified, and matched byusing image frames after the loader leaves the cargo area to framesbefore the loader entered the cargo area.

In one embodiment, the system 100 performs the process 500 to trackpackages continuously after they have been scanned, loaded, and“matched”. The process 500 enables tracking of matched packages withinan area of coverage after a package has been identified (“marked”).Specifically, after a package is loaded and marked in one place, theimage processing CPU 122 can regularly (or continuously) perform thesame (or similar) threshold detection to search for a “change” at thelocation of interest. This accounts for object movement duringtransport.

In this scenario, the system 100 has identified packages within the areaof coverage and no new packages have been scanned. This may representwhen the driver is driving the vehicle to a destination. If the imageprocessing CPU 122 detects a change at or near a package location, atracking subroutine is invoked. The detection of a change may comprisean image absolute difference comparison between frames as previouslydescribed with respect to detailed image processing. The processor 122analyzes the location of the package within the image at which thechange occurred and determines if the package at that location stillmatches the data for the package captured off the barcode. If the matchis identical, the system 100 may continue to label the package ascorresponding to the package scanned and placed at that location.

If, however, no package is detected at the location or if the packagedimensions do not match the expected package dimensions with a highlevel of confidence, the image processor 122 searches for an“unidentified” package that matches the moved package dimensions. Whenthe matching package is located, its overlay marking on the cargo systemis updated to display the new package location.

The above ability to identify movement of previously located packages isparticularly valuable in delivery vehicles. Drivers often shift packagesforward in the vehicle during the delivery day to make packagesaccessible. By monitoring known package locations and tracking themovement of a package to a new location, the system 100 maintains a realtime map of package locations.

In another embodiment, the system 100 can be configured to reducepotential human loading errors that occur from a breakdown of asequential loading pattern of scanning a package then loading thatpackage immediately into truck. This reduction may be achieved by, forexample, providing additional scanners over the delivery vehicle loadingdoors to scan bar codes automatically as packages are placed into thevehicle. Such a system can guarantee that the packages scanned are thepackages loaded into the truck. After a package is scanned, it is alsoviewed by the optical sensors in the vehicle; that direct and almostsimultaneous registration improves package identification.

In another embodiment, the system 100 can alternatively providecontinuous, real time tracking, albeit with more complicated imageprocessing. In such a system, for example, a person (loader, driver,etc.) may be identified and the system may detect objects located in thevicinity of the hands of the person to determine if the object matchesthe package expected to be loaded. Further, an algorithm for identifyinga package or its unique identifier (size, color, etc.) may be tailoredto specific environments or hardware. The tradeoff of such a fullreal-time tracking system is increased system complexity.

In another embodiment of the system 100, an augmented reality (“AR”)real time video view may be presented to the loader/driver. For AR videoin real time, a single perspective is shown of the vehicle cargo mapwith those designated packages needing to be taken being highlighted orlit. The user may view one perspective of the vehicle from the front (orback, depending on how the user is removing the packages, that is, fromeither the front or from the back), one perspective of the left side ofthe vehicle and one perspective of the right side of the vehicleassociated with each camera. The image processing CPU 122 may determinewhere the driver/delivery person is and provide a perspective on thetablet based on the driver position in relation to the package beingdelivered. As previously described, identifying the user position withinthe area of coverage is analogous to identifying a package.

Additional package delivery data may be gathered using the presentsystem. For example, the system 100 may track package movement in realtime. Therefore, tracking package movement, especially velocity, canhelp prevent mistreatment of packages through packages being thrown,dropped, or placed in positions that are not secure and risk having thepackages fall. By tracking packages movement in real time anddetermining movement velocity, impact through rough handling can bemonitored and reported to improve the quality of the loading andunloading procedures and to prevent damage to the packages. In thisembodiment, velocity may be determined by dividing the distance apackage moves by the frame rate in which such movement occurs.

FIGS. 6A, 6B, and 6C together show an embodiment of an image-processingprocess 600 that uses optical information supplemented with depthinformation to track a package, product, or item. Specifically, as isfamiliar to one of ordinary skill in the art, a two-dimensional (2D)optical image capture device (i.e., a camera) with a single aperture iscapable of capturing 2D image information on a plane (film, CCD, etc).To acquire three-dimensional (3D) information typically requiresacquisition of additional data. Three-dimensional data can be acquiredusing multiple cameras or by combining one or more cameras with one ormore depth sensors. Cameras can utilize visible light, infrared light,or other optical wavelength ranges. Depth sensors can be based oninfrared, laser or other wavelength emitters that transmit light to anobject, or to a portion of the object. Depth sensors typically determinethe distance to the object, or to portion of the object, from the lightthat is reflected or backscattered from the object. Alternatively, depthsensors can utilize acoustic signals to determine distance. In oneembodiment, depth sensing is integrated into the optical camera, forexample, the KINECT™ K000949, although other devices can be used.

Referring to FIG. 6A, at step 602, frames are acquired from the camerasystem. A camera system with depth sensing capability typically outputsvideo (e.g., RGB, CYMG) and depth field information. Video mayoptionally be encoded to a well known format, such as MPEG. At step 604,the optical and depth information are stitched together. Open librariessuch as OpenCV or OpenNI (used to capture depth images) enable theoptical and depth information to be stitched together. Alternatively, auser may develop customized software for generating 3D information forobject data generated by optical images and depth sensors.

At step 606, an initial calibration is performed if a calibration hasnot been previously performed. A function of this initial calibration,which is performed over multiple image frames, is to determinebackground information both for 2D optical images and depth sensing. Anymotion (e.g., people) is extracted or ignored (step 608) duringbackground extraction until stable background optical (RGB) and depthinformation can be stored (step 610). Calibration may optionally includecreation of a foreground or front-ground region. This front regionlimits the data set for analysis to a region near shelves where objectsof interest (e.g., packages) are to be located. Calibration may beperformed on start-up, at intervals, be initiated by the user, or by thesystem, for example, if errors are detected.

After calibration is complete, the resulting spatial filter masks areused to extract the “area of interest.” In one embodiment, this area ofinterest corresponds to the area between the background and theforeground, so everything that is not the wall and the shelves (forbackground) and not the person in front of the shelves, is ignored. Thisignoring of the background and foreground focuses on data within thedepth threshold of the area of interest being monitored. Alternatively,the “area of interest” can include a different part of the scene, forexample, the foreground in order to see where the person is in laterrecognition steps and can be expanded or contracted as systemrequirements dictate. In general, the area of interest applies to anycut-out of a scene that is to be the focus within which to performobject tracking.

Multiple image frames (e.g., N−1 and N) are obtained (step 612) andcompared (step 614), similarly to that performed in process 500 (FIG. 5), although the image frames in the process 600 include depthinformation in addition to RGB data. Image and depth information can befiltered for noise and then processed to determine if a differencebetween two frames exists. This can be done with edge detection,threshold and difference algorithms, or other image processingtechniques. In the process 600, information from the depth sensor isalso processed to compare image frames.

Referring to FIG. 6B, when no image change is found (step 618), that is,when depth and optical data remain substantially unchanged, the process600 continues with the next temporal images received (e.g., N and N+1).When a change is detected, the process 600 determines (step 620) whethera “background” object has moved. If a background object has not moved,the process 600 continues with the next temporal images received (e.g.,N and N+1). If a background object is determined to have moved, thesystem 100 does not have to consider a package for tracking, and furthergeneral tracking continues. In this instance, the system 100 may go backto the calibration step to establish a new stable background data sethaving 2D optical image and depth information.

In one embodiment, the process 600 compares two frames of imageinformation for change, ignoring the background/foreground masks; anyactual change in the image triggers further analysis. However, it isless processing and power intensive to detect only changes in the “areaof interest” between the background and foreground (if foregroundmasking is utilized). When the background is stable, at step 622absolute background subtraction is performed (likewise for foreground).This step allows the resulting 3D information to be processed faster fordetermining areas of interest in which one or more new packages may bypresent. Absolute image subtraction may be formed using OpenCV librarymodules in one embodiment, though other alternative techniques may alsobe used.

With the background information (and foreground if applicable)subtracted, the process 600 checks (step 624) for changes in depth ofany objects in the field of view of the camera(s) and the measurementfield of the depth sensor(s). If no changes are found and no package hasbeen scanned (step 626), this indicates that no package has beendetected and the next images are processed (step 602). However, if apackage was scanned (step 626), but no package was detected, the process600 can use (step 628) historical optical and depth information (orinformation from an adjacent wireless tracking system) to register thatthe last scanned package has not been located, indicate the last knownlocation of the package, and inform the user of the ambiguity.

Referring now to FIG. 6C, if at step 624 a change in the depth of one ormore objects has been detected, an area of interest around that regionof change is generated (step 630). In one embodiment, an area ofinterest is generated using a software module from the OpenCV library,though other techniques may be employed. Typically, though notnecessarily, the area of interest also includes movement information orvector information that indicates object motion.

When the area of interest is determined, a “point cloud” is generated(step 632) using the optic sensor(s) extrinsic and intrinsic parametersthrough algorithms for “2D to 3D” data representation conversionpreformed on the RGB and/or depth images obtained and processed throughOpenNI and OpenCV. In one embodiment, the Point Cloud Library may beused. The object shape and location information generated from the PointCloud Library are used to identify and track a package in threedimensions using edge detection, color detection, object recognitionand/or other algorithms for determining an object within the scene. Ifobject information is in the shape of a human, for example, then theprocess 600 continues processing further image data and does not trackthe human (unless the system 100 tracks user motion). However, if thesize, shape or other appearance information indicates that the object isa package, the object is recorded as such. The process 600 resolves(step 634) the identity of a plurality of scanned packages based on thisinformation by comparing expected package size, shape and/or appearanceattributes (as established by information associated with scanning apackage) with measured information. The use of both optical and depthsensing information allows the system to calculate package size based onthe 3D data generated from the camera images and depth sensor data. Theidentity, location and other information (e.g., time of placement andmotion) may be stored at a central server (e.g., 204 of FIG. 2 ) forlater analysis.

When an object is detected and matches a scanned package in size andappearance, the object is registered. A variety of reasons exist for adetected object not to match a scanned package. For example, the objectmay be partially occluded or a different object may have beensubstituted. In some instances, further analysis on subsequent imageframes is performed to resolve the object size and appearance. In suchinstances, further image processing occurs until the object isidentified or marked unidentified (step 636).

The aforementioned description of the process 600 is with respect to apositive change in an image scene: specifically, a new object islocated. A “negative change” can also be detected in a similar fashionand occurs when a package is removed from an area of interest. In such asituation, a difference is not mistaking package occlusion as objectremoval. Specifically, if a person steps in front of a package, then thesystem detects the motion and shape of the person. After the personmoves away from the front of the package, the image processor 122detects if the identified package was removed. Note that the usertypically scans a package when moving it, so taking a package from alocation without scanning it may trigger a flag to the user to scan oridentify the package.

In many situations, a second package may be placed so as to partiallyocclude a first registered package. In those instances, the system 100looks for evidence based on depth and size information that the firstpackage is still in its original location. Such evidence can be a cornerof the package remaining visible behind the second package. If the firstpackage is fully occluded, but not scanned to indicate its removal, thenthe system 100 may be designed to assume the first package is sittingbehind the larger second package.

As previously described, the system 100 detects changes in a field ofview to build a database of known packages. The database is used tolocate and disregard these registered packages while looking foridentifying new objects being placed into the field of view. While theregistered packages are “disregarded” when looking for new packages thatare being loaded, they are continually monitored to see if they havemoved or been removed.

The process 600 may run continuously or be triggered upon user startup,detection of motion, or other triggers. Allowing the system 100 to dropto a lower state of analysis may be desirable in some instances toreduce bandwidth and power consumption. For example, if a deliveryvehicle is being loaded, then the system 100 can run at full speed withprocessing of images at the maximum rate described by the camera.However, after loading is complete, the system 100 can operate atintervals (for example, by processing images once every 3 seconds) toconserve power, data storage and bandwidth while meeting therequirements of the specific application.

Augmented Package Loading Techniques

Package tracking systems described herein can track packages withinconventional delivery systems wherein loaders place packages on vehiclesaccording to their perception of proper loading protocols. Thisperception may vary by loader, region, delivery vehicle, or otherfactors. Such package tracking systems can also be configured tooptimize package loading in addition to delivery. In one example, thecentral server 204 (FIG. 2 ) or image processor CPU 122 (FIG. 1 ) maykeep a list of all packages intended for placement on a particulardelivery vehicle. In this example, the package identificationinformation for each package includes the intended addressee and packagesize information. The intended addressees are used to generate an orderof delivery that may be used to place packages in a specific order inthe delivery vehicle (e.g., packages to be delivered first are put in aposition of easiest access). Package size may also be a factor affectingpackage loading. Heavy or large packages can be located on the floor oran appropriate (i.e., low) shelf, irrespective of the delivery order.

In one embodiment, when the loader scans a package and enters thedelivery vehicle with the package, the CPU 122 activates a light thatshines on the location for that package. The location and matching ofthe package may be confirmed as previously described. A focused lightmay be used to identify the proper loading place for the package. Thesource of the light can be the same light as that used to identify apackage for a driver.

In the various embodiments detailed herein, the location of a packagemay be “marked” or indicated in a variety of manners: by projectinglight on the package of interest (unidentified package, package to bedelivered, etc.), by projecting light where the package is to be loaded,by marking the position of the package on a live camera feed of thecargo bay, in a representational view of the cargo bay with the packagelocation identified, or in a projection of the marking in augmentedreality glasses.

As an example of light-based guidance for package loading, consider asystem that employs conveyor belts to move packages inside a facility.As the packages are transported on the conveyor belt they are scannedfor identification, either by optical, magnetic, or electromagneticmeans. After each package is identified, the system continually monitorsthe position of the package as it moves from one area of the facility tothe end destination for transportation vehicle loading. As packagesreach areas for vehicle loading, the system uses a form of lightguidance to help loaders identify proper vehicle package assignment. Forexample, if a package is assigned to particular truck, that truck couldbe assigned a particular color, say blue. The package designated for theblue truck is then illuminated with a blue light, through LED, laser, orrelated light guidance means, thus making package vehicle identificationeasy for loaders. After the loader places the package in the identifieddelivery truck, the package tracking system can detect its presence andregister its location as previously described.

One of ordinary skill in the art will recognize that other cues (visual,auditory or the like) using various technologies may be used to markpackage location for easy loading, delivery or tracking of packages.

Augmented Tracking

Various embodiments of the package tracking systems described herein maybenefit from additional tracking technology. For example, in the biggerareas (e.g., freight, air cargo, large shipping containers), one mayincorporate other techniques to make tracking more interactive, such asUltra-wideband (UWB) or Wireless Lan (including, but not limited to,802.11 protocol communications or the like). Example implementations oftechniques for tracking can be found in U.S. patent application Ser. No.14/614,734, filed Feb. 5, 2015, titled “Virtual Reality and AugmentedReality Functionality for Mobile Devices,” the entirety of which ishereby incorporated by reference.

In a package tracking system that augments optical tracking with UWBtracking, the driver, the driver's tablet, the packages, or all of theabove, are actively tracked as described in U.S. patent application Ser.No. 15/041,405, filed Feb. 11, 2016, titled “Accurate GeographicTracking of Mobile Devices,” the entirety of which is incorporated byreference herein. In one embodiment, the position of the driver's tabletis tracked so that the viewpoint from the tablet's camera associatedwith the tablet location and orientation is streamed to the tablet, withdigital images overlaid onto the tablet's camera view, and is used fornavigation or package identification. In this example, as the tabletcamera views a stack of packages, the accurate tracking of the physicalposition and orientation of the tablet allows the system to overlay adigital image, for example, a flashing red light, on top of the packagethat is seen by the tablet camera. In this case, digital images areshown on the tablet camera view, not projected onto the actual packageby an external light source.

Small delivery (and other delivery modes, like airfreight, cargocontainers) may use of UWB or RF (radio frequency) to improve positionalaccuracy tracking for when and where packages are scanned. The packagesmay be tracked using UWB with tags on the packages until a handoff tothe camera for optically tracking inside the delivery vehicle becomespossible. This is a benefit as it reduces or eliminates the need to dooptical image processing in the delivery vehicle, but still providespackage ID confirmation and tracking (which may then also bere-registered via dimension data inside the delivery vehicle by thecameras).

In addition, cumulative tracking methods (i.e., optics and UWB) helptrack the driver and packages. For example, in dark environments, largeenvironments or in situations involving other issues with opticalcoverage, it may be preferable to use UWB or related RF-based trackingto identify initial package location, and to switch to optical scanningafter package location is generally identified. In such situations, UWBtracking may augment or supplant optical tracking.

Also, in some situations, one may want to track the loader using a tagphysically associated with that person. In such an environment, one mayscan a package and then track the loader using UWB to make sure thepackage goes to the correct delivery vehicle (for instance, they may beloading multiple trucks) or, in other use cases, track the driver as thedriver leaves the delivery vehicle to insure proper delivery drop offlocation. In the scenario where a driver is being tracked, the driver istracked as he leaves the delivery vehicle with the GPS position knowneither on the delivery vehicle or on the driver. As the driver leavesthe delivery vehicle, the driver is tracked and when the package isdropped off, the package is scanned and the position in relation to thedelivery vehicle is recorded to show proof of delivery. As described inthe aforementioned U.S. patent application Ser. No. 15/041,405,augmented reality (AR) glasses can be used to track a driver. In thisscenario, the AR glasses are being tracked by a form of RF tracking, andthe orientation and position of the driver may be determined by theglasses.

Example implementations of UWB or other wireless tracking systems aredescribed disclosed in U.S. patent application Ser. No. 13/975,724,filed Aug. 26, 2013, titled “Radio Frequency Communication System”, theentirety of which is incorporated by reference herein. Tracking may beimplemented outside the delivery to confirm that a package that wasscanned by glasses or a finger scanner is the same package that getsloaded into the delivery vehicle. In such scenarios, a loader scans thepackage off a conveyor belt, and the loader is tracked by the UWB systemto ensure that the package scanned is the package placed in the truck oris at the proper loading area of the delivery vehicle. Thereafter, theoptical tracking system tracks packages within the area of coverage.

FIG. 7 shows a diagram of an embodiment of a package tracking system 700including an optical tracking hub 702 augmented with a radio frequency(RF) positioning system 704. The package tracking system 700 includes auser device 706 and a cloud-based central server system 708. The hub 702is deployed in an area 710 used to hold packages, assets, objects,items, or the like, and is in communication with the cloud-based centralserver system 708 over a wireless communications channel (e.g.,cellular) 716. Depending on the service provider for cellular PHYcommunications, if the holding area (e.g., delivery truck) is outside ofthe service area, the hub 702 buffers the data, package identificationinformation, transactions, etc., until the holding area comes into rangeof a facility with secure Wi-Fi (i.e., provided, for example, by thepackage delivery company). For purposes of enabling customers to push orpull data from the cloud-based central server system 708, the hubprovides a “Cloud” API (application program interface).

The RF positioning system 704 includes four RF nodes 712-1, 712-2,712-3, and 712-4 (generally, 712) and an RF tag 714. The RF positioningsystem 704 operates to track the position of the RF tag 714, which canbe affixed to the package or worn by personnel, such as a driver orpackage loader. In general, the RF nodes 712 provide an interface overWi-Fi to the user device 706. The RF nodes 712 are in communication withthe user device 706 via Wi-Fi, and the user device 706 is incommunication with the hub 702 via Wi-Fi; in effect, the hub 702provides an ad hoc Wi-Fi hotspot to the user device 706 and RF nodes712.

The user device 706 is any computing device capable of runningapplications and wireless communications. Examples of the user device706 include, but are not limited to, tablets and smart phones. The userdevice 706 can be in communication with the hub 702 over a wirelesscommunications link 718, with the server system 708 over a wirelesscommunications link 720, or both. An example implementation of thecommunication links 718, 720 is Wi-Fi.

The area 710 for holding assets can be stationary or mobile. Astationary holding area can be disposed anywhere along the deliverychain, from a warehouse to a package delivery center. Examples ofstationary holding areas include, but are not limited to, package rooms,closets, warehouses, inventory rooms, storage rooms, and trailers.Examples of mobile holding areas include, but are not limited to,delivery trucks, tractor trailers, railway cars, shipping containers,and airplane cargo bays. Each holding area (i.e., each facility, truck,etc.) is equipped with an optical tracking hub 702. An example of adelivery truck than can be equipped with an optical tracking hub 702 isthe standard Ford® P1000.

The RF tag 714 is in communication with the user device 706 over awireless communication link 722, for example, Bluetooth, and with the RFnodes 712 by way of RF signals 724.

During operation, in general the hub 702 provides interior tracking(e.g., inside a delivery vehicle) of a package using optical techniquesand the RF positioning system 704 provides exterior tracking (e.g.,outside of the delivery vehicle) of the RF tag 714 using RF signals. Inone embodiment, the user device 706 directly communicates with theserver system 708 (e.g., in the cloud). In another embodiment, the userdevice 706 provides data to the hub 702, and the hub 702 communicateswith the server system 708. In this embodiment, any feedback informationfrom the server system 708 goes through the hub 702, which communicatessuch information to the user device 706 by Wi-Fi.

FIG. 8 is a schematic for an embodiment of a package tracking system 800including a holding area 802, configured for optical tracking andaugmented with RF tracking, in communication with a hub and powersubsystem 804. The holding area 802 includes four RF nodes 806-1, 806-2,806-3, 806-4 (generally, 806) with antennae, three cameras (with depthsensors) 808-1, 808-2, 808-3 (generally, 808), and an optional monitoror display device 810 (e.g., an HDMI video display, with or without aspeaker) to provide a visual status of the system 800. In oneembodiment, the three cameras 808 are USB3-based. Each RF node 806,camera 808, and display device 810 is connected to a power bus 812(e.g., a 12 VDC). The holding area 802 can also include a lightprojector (not shown) to shine focused light, or a particular colorlight, on the location within the area, to show personnel where aparticular package can be currently found or where a particular packagebeing loaded should be placed.

The hub and power subsystem 804 includes an image processor 814, a powersubsystem 816 connected to a power source 818, and an optional charger820. The power subsystem 816 provides power to the image processor 814and charger 820 by the power bus 814. In one embodiment, the powersource 818 is a battery (e.g., 12 VDC, 55 aH). An accessory power source838 is connected to the power subsystem 816. In communication with theimage processor 814 is a cellular antenna 822, a GPS antenna 824 and aWi-Fi antenna 826. The image processor 814 is also in communication withthe cameras 808 by communication links 828 and with the optional displaydevice 810 by communication link 830. Also shown are the user device832, RF tag 834, and scanner 836. An optional light projector externalto the holding area 802 (not shown) can be used to shine light on apackage before the package is loaded, for purposes of guiding a loaderto the location where the package is to be loaded (e.g., a particulardelivery truck).

In one embodiment, the image processor 814 is implemented with abCOM6-L1400 Express Module produced by General Electric of Fairfield,Conn. The interfaces of the image processor 814 include: at least threeUSB3 ports for connecting to the cameras 808 and a USB2 port forconnecting to an optional light-projector gimbal; an HDMI port forconnecting to the display device 810; an integral GPS unit with theexternal GPS antenna; a cellular PHY card/interface (e.g., LTE, GSM,UMTS, CDMA or WCDMA, or WiMAX) with a cellular antenna jack (for anappropriate multiband cellular antenna operating at 800-950 MHz,1800-1900, 1900-2000, 2100-2200 MHz bands, and can be a differentphysical antenna depending on the cellular PHY provider chosen for thegiven area) to enable a wireless connection to a cellular data serviceprovider; and a Wi-Fi module with a Wi-Fi antenna jack (the antenna isomni-directional, providing 500 m of range, and operating over the2400-2480 MHz range).

The holding area 802 can be stationary or mobile. For a mobile holdingarea 802, such as a delivery truck, the RF nodes 806 can be mountedexternally on the roof of the cargo area at the four corners, with thecameras 808 and display device 810 mounted internally within the holdingarea 802. All of the cameras 808 are mounted near the ceiling of thetruck box, facing towards the back of the truck, one camera at eachfront corner of the truck box, with the third camera at the front of thetruck box disposed between the other two cameras. The cellular antenna822 and Wi-Fi antenna 826 are mounted inside the truck and the GPSantenna 824 is mounted on the roof. In addition, a standard small formfactor 2-axis gimbal can be mounted to the ceiling or rafter of thetruck box. The gimbal provides azimuth (180 degree) and elevation angle(90 degree) positioning of the optional interior light projector (e.g.,a laser pointer), which can be turned on and off. A USB2 interface ofthe image processor to a light projector sets the azimuth, elevation,and on/off state of the light.

The hub and power subsystem 804 can be placed within the cab of thetruck, for example, behind the driver's seat. The system 800 is notattached directly to the vehicle DC power terminals, or directly to thebattery of the vehicle, to avoid draining the battery of the deliveryvehicle. Power subsystem 818 can connect to the accessory power 838 ofthe vehicle on a fuse. When the delivery vehicle is parked and off, theaccessory power 838 is turned off, and the system 800 runs on theinternal battery 818. The battery 818 thus ensures that when thedelivery vehicle is off (such as during package loading) the variouscomponents of the system 800 remain powered. When the vehicle is idlingor in motion, the system 800 charges the battery 818. The powersubsystem 818 also provides 12 VDC and 5 VDC dedicated for the RF Nodes806 and the cameras 808.

For a stationary holding area 802, the RF nodes 806 can be mountedexternally near an entrance to the area 802, with the cameras 808 anddisplay device 810 installed inside. The hub and power subsystem 804 canalso be installed inside or outside of the holding area 802. For astationary holding area 802, the cellular antenna 822 and GPS antenna824 are optional.

A schematic diagram, as shown in FIG. 8 , without the RF nodes 806 andRF tag 834, can illustrate an embodiment of a package tracking systemthat is not augmented with RF tracking.

As will be appreciated by one skilled in the art, aspects of the systemsdescribed herein may be embodied as a system, method, and computerprogram product. Thus, aspects of the systems described herein may beembodied in entirely hardware, in entirely software (including, but notlimited to, firmware, program code, resident software, microcode), or ina combination of hardware and software. All such embodiments maygenerally be referred to herein as a circuit, a module, or a system. Inaddition, aspects of the systems described herein may be in the form ofa computer program product embodied in one or more computer readablemedia having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. The computer readablemedium may be a non-transitory computer readable storage medium,examples of which include, but are not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination thereof.

As used herein, a computer readable storage medium may be any tangiblemedium that can contain or store a program for use by or in connectionwith an instruction execution system, apparatus, device, computer,computing system, computer system, or any programmable machine or devicethat inputs, processes, and outputs instructions, commands, or data. Anon-exhaustive list of specific examples of a computer readable storagemedium include an electrical connection having one or more wires, aportable computer diskette, a floppy disk, a hard disk, a random accessmemory (RAM), a read-only memory (ROM), a USB flash drive, annon-volatile RAM (NVRAM or NOVRAM), an erasable programmable read-onlymemory (EPROM or Flash memory), a flash memory card, an electricallyerasable programmable read-only memory (EEPROM), an optical fiber, aportable compact disc read-only memory (CD-ROM), a DVD-ROM, an opticalstorage device, a magnetic storage device, or any suitable combinationthereof.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. As used herein, acomputer readable storage medium is not a computer readable propagatingsignal medium or a propagated signal.

Program code may be embodied as computer-readable instructions stored onor in a computer readable storage medium as, for example, source code,object code, interpretive code, executable code, or combinationsthereof. Any standard or proprietary, programming or interpretivelanguage can be used to produce the computer-executable instructions.Examples of such languages include C, C++, Pascal, JAVA, BASIC,Smalltalk, Visual Basic, and Visual C++.

Transmission of program code embodied on a computer readable medium canoccur using any appropriate medium including, but not limited to,wireless, wired, optical fiber cable, radio frequency (RF), or anysuitable combination thereof.

The program code may execute entirely on a user's device, partly on theuser's device, as a stand-alone software package, partly on the user'sdevice and partly on a remote computer or entirely on a remote computeror server. Any such remote computer may be connected to the user'sdevice through any type of network, including a local area network (LAN)or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider).

Additionally, the methods described herein can be implemented on aspecial purpose computer, a programmed microprocessor or microcontrollerand peripheral integrated circuit element(s), an ASIC or otherintegrated circuit, a digital signal processor, a hard-wired electronicor logic circuit such as discrete element circuit, a programmable logicdevice such as PLD, PLA, FPGA, PAL, or the like. In general, any devicecapable of implementing a state machine that is in turn capable ofimplementing the proposed methods herein can be used to implement theprinciples described herein.

Furthermore, the disclosed methods may be readily implemented insoftware using object or object-oriented software developmentenvironments that provide portable source code that can be used on avariety of computer or workstation platforms. Alternatively, thedisclosed system may be implemented partially or fully in hardware usingstandard logic circuits or a VLSI design. Whether software or hardwareis used to implement the systems in accordance with the principlesdescribed herein is dependent on the speed and/or efficiencyrequirements of the system, the particular function, and the particularsoftware or hardware systems or microprocessor or microcomputer systemsbeing utilized. The methods illustrated herein however can be readilyimplemented in hardware and/or software using any known or laterdeveloped systems or structures, devices and/or software by those ofordinary skill in the applicable art from the functional descriptionprovided herein and with a general basic knowledge of the computer andimage processing arts.

Moreover, the disclosed methods may be readily implemented in softwareexecuted on programmed general-purpose computer, a special purposecomputer, a microprocessor, or the like. In these instances, the systemsand methods of the principles described herein may be implemented asprogram embedded on personal computer such as JAVA® or CGI script, as aresource residing on a server or graphics workstation, as a plug-in, orthe like. The system may also be implemented by physically incorporatingthe system and method into a software and/or hardware system.

While the aforementioned principles have been described in conjunctionwith a number of embodiments, it is evident that many alternatives,modifications, and variations would be or are apparent to those ofordinary skill in the applicable arts. References to “one embodiment” or“an embodiment” or “another embodiment” means that a particular,feature, structure or characteristic described in connection with theembodiment is included in at least one embodiment described herein. Areference to a particular embodiment within the specification do notnecessarily all refer to the same embodiment. The features illustratedor described in connection with one exemplary embodiment may be combinedwith the features of other embodiments. Accordingly, it is intended toembrace all such alternatives, modifications, equivalents, andvariations that are within the spirit and scope of the principlesdescribed herein.

What is claimed is:
 1. A method for tracking a package within an area,comprising: obtaining package identification information from ascannable medium associated with a package; acquiring image informationof the area; detecting a presence and location of an object in the areathat matches the package based on a comparison of the packageidentification information obtained from the scannable medium with theimage information of the area obtained by the optical sensing device;and registering the package as being present in the area at the locationof the detected object in response to detecting a match between theobject and the package.